Aditi Krishnapriyan

1/ A grand challenge in science: the simulation-to-experiment gap

Simulators model complex systems but introduce model errors; experiments give real-world data but are costly and noisy

Can we leverage both? Yes! We introduce Adversarial Distribution Alignment (ADA), at #NeurIPS2026

ADA aligns generative models—initially trained on simulations (e.g., molecular dynamics with classical force fields)—to full real-world distributions (e.g., protein structures) using only partial, noisy observations (like cryo-EM images)

ADA goes beyond just matching the expectation of observables: it can match full distributions, even in the presence of multiple correlated, high-dimensional observations

Check out our website: kaityrusnelson.com/ada/
And our paper: arxiv.org/abs/2510.02259

In collaboration with @KaiTyrusNelson, @tobykreiman, and @svlevine

1/ How can we improve our models of the physical world? We develop EddyFormer: integrating spectral methods w/ the Transformer architecture. We can accelerate 3D turbulence simulation by up to 30x compared to top numerical solvers, at the same level of accuracy!

At #NeurIPS2025 on Dec 4 at 4:30 PM, Exhibit Hall C, D, E: #2316

Project page: mrlazy1708.github.io/eddyformer

At this point the AI for Science community should stop focusing on achieving "state-of-the-art” on datasets like QM9 & MD17: chasing small improvements on these outdated datasets is scientifically meaningless. It's like telling vision researchers to ditch internet-scale and go back to benchmarking on MNIST/CIFAR10

1/ Can molecular AI move past hard-coded Graph Neural Networks and embrace scalable Transformers that discover molecular structure on their own?

We demonstrate that you can train a 1B parameter Transformer model without any graph priors or physical inductive biases.

And surprisingly, not only can it maintain competitive performance under equal compute on the Open Molecules 2025 dataset… it’s faster than a 6M parameter equivariant GNN, and exhibits scaling laws that don’t saturate. We use this as a starting point to investigate emergent internal representations, and find that it adaptively discovers molecular structure!

Check out the interactive demo on our website: tkreiman.github.io/projects/graph…

And our paper: arxiv.org/abs/2510.02259

In collaboration with @tobykreiman, @YutongBAI1002, Fadi, Elizabeth, and @EricQuCal.

Here’s a video showing how the Transformer learns distance-aware attention patterns (purple gradient) that adapt to atomic environments 👇

1/ Generating transition pathways (e.g., folded ↔ unfolded protein) is a huge challenge: we tackle this by combining the scalability of pre-trained, score-based generative models and statistical mechanics insights-—no training required! To appear at #ICML2025